ParamHelpers (1.14)

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Helpers for Parameters in Black-Box Optimization, Tuning and Machine Learning.

Functions for parameter descriptions and operations in black-box optimization, tuning and machine learning. Parameters can be described (type, constraints, defaults, etc.), combined to parameter sets and can in general be programmed on. A useful OptPath object (archive) to log function evaluations is also provided.

Maintainer: Jakob Richter
Author(s): Bernd Bischl [aut] (<>), Michel Lang [aut] (<>), Jakob Richter [cre, aut] (<>), Jakob Bossek [aut], Daniel Horn [aut], Karin Schork [ctb], Pascal Kerschke [aut]

License: BSD_2_clause + file LICENSE

Uses: backports, BBmisc, checkmate, fastmatch, akima, ggplot2, lhs, plyr, testthat, GGally, emoa, gridExtra, reshape2, eaf, irace, covr
Reverse depends: cmaesr, ecr, mlr, mlrCPO, mlrMBO, randomsearch, smoof
Reverse suggests: ChemoSpec2D, dynparam, flacco, llama, OpenML
Reverse enhances: liquidSVM

Released 5 days ago.

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